RENAISSANCE does not require training data to parameterize nonlinear dynamic models of metabolism. Instead, it uses evolution strategies, thus allowing specialists and non-specialists to efficiently create large-scale kinetic models. How? Check out: biorxiv.org/content/10.110…

Really happy to have this preprint out! Quick thread about our work:

Congrats to Asli Sahin, Daniel R. Weilandt, Ph. D., and @Vassily_13 ! Amazing work!

Our latest work on simulating plasmid burden using ME-models is now available at biorxiv.org/content/10.110…! Congratulations to Omids (Omid Oftadeh) and Vassily (Vassily Hatzimanikatis)!

Excited to announce that our latest work in now online ! This time the team significantly expanded on their previous tool, BridgIT, an enzyme annotation for orphan and novel reactions, to bring you the new and improved BridgIT+ ! Read the thread below to find out more :)

Curious and want to dive deeper? Check out our preprint on Bioarxiv: biorxiv.org/content/10.110…

Can we efficiently use dynamic nonlinear models to devise genetic interventions for desired cellular phenotypes? Check out in nature.com/articles/s4146… just published in Nature Communications. Congratulations to Bharath (Bharath Narayanan), Daniel (Daniel R. Weilandt, Ph. D.), Maria, and @Vassily_13.

Our recent publication in Nature Communications, “Rational strain design with minimal phenotype perturbation” by Narayanan et al., has been showcased in the recent Editors’ Highlights for Biotechnology and Methods focus (nature.com/collections/id…).

Want to efficiently create large-scale dynamic models of metabolism that fit experimental data and reliably predict metabolic responses to various perturbations? Our new method does just that! Check it out here: nature.com/articles/s4192….

The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

This preprint biorxiv.org/content/10.110… proposes a systematic framework to repurpose generative neural nets across physiological contexts enabling efficient construction of kinetic models tailored to specific scenarios. A step toward flexible/scalable modeling! #AI #SystemsBiology

Excited to share our new preprint, where we present NIS, a framework that distills GEMs into interpretable modules, enabling direct cross-species comparisons of fueling pathways, biosynthesis, and environmental exchanges. Congratulations to the authors!

Our review made the cover in ACS Synthetic Biology! 🎉🧬 We explore how large-scale kinetic models are shaping the future of metabolic engineering — check it out here 👉 pubs.acs.org/toc/asbcd6/14/8 Big thanks to the team and ACS Publications! #MyACSCover #SyntheticBiology

🚀 New preprint from @epfl_en’s Laboratory of Computational Systems Biotechnology (LCSB)! We present “Multi-omics–driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer.” 🔗 biorxiv.org/content/10.110…

By integrating multi-omics data with enzyme kinetics, we generated a population of near–genome-scale kinetic models that capture BRCA1-mutant vs wild-type ovarian cancer physiology. These models reveal network-wide control principles & drug-response dynamics.

This work by Toumpe, Masid, Hatzimanikatis & Miskovic (@epfl_en LCSB) provides a resource for exploring metabolic regulation, vulnerabilities, and precision oncology. All models are openly available. #systemsbiology #cancermetabolism #multiomics #precisiononcology #openscience